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1.  Collaborative modeling of the impact of obesity on race-specific breast cancer incidence and mortality 
Obesity affects multiple points along the breast cancer control continuum from prevention to screening and treatment, often in opposing directions. Obesity is also more prevalent in Blacks than Whites at most ages so it might contribute to observed racial disparities in mortality. We use two established simulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate the impact of obesity on race-specific breast cancer outcomes. The models use common national data to inform parameters for the multiple US birth cohorts of Black and White women, including age- and race-specific incidence, competing mortality, mammography characteristics, and treatment effectiveness. Parameters are modified by obesity (BMI of ≥30 kg/m2) in conjunction with its age-, race-, cohort- and time-period-specific prevalence. We measure age-standardized breast cancer incidence and mortality and cases and deaths attributable to obesity. Obesity is more prevalent among Blacks than Whites until age 74; after age 74 it is more prevalent in Whites. The models estimate that the fraction of the US breast cancer cases attributable to obesity is 3.9–4.5 % (range across models) for Whites and 2.5–3.6 % for Blacks. Given the protective effects of obesity on risk among women <50 years, elimination of obesity in this age group could increase cases for both the races, but decrease cases for women ≥50 years. Overall, obesity accounts for 4.4–9.2 % and 3.1–8.4 % of the total number of breast cancer deaths in Whites and Blacks, respectively, across models. However, variations in obesity prevalence have no net effect on race disparities in breast cancer mortality because of the opposing effects of age on risk and patterns of age- and race-specific prevalence. Despite its modest impact on breast cancer control and race disparities, obesity remains one of the few known modifiable risks for cancer and other diseases, underlining its relevance as a public health target.
PMCID: PMC3511695  PMID: 23104221
Simulation modeling; Breast cancer; Disparities; Obesity
2.  Breast density as indicator for the use of mammography or MRI to screen women with familial risk for breast cancer (FaMRIsc): a multicentre randomized controlled trial 
BMC Cancer  2012;12:440.
To reduce mortality, women with a family history of breast cancer often start mammography screening at a younger age than the general population. Breast density is high in over 50% of women younger than 50 years. With high breast density, breast cancer incidence increases, but sensitivity of mammography decreases. Therefore, mammography might not be the optimal method for breast cancer screening in young women. Adding MRI increases sensitivity, but also the risk of false-positive results. The limitation of all previous MRI screening studies is that they do not contain a comparison group; all participants received both MRI and mammography. Therefore, we cannot empirically assess in which stage tumours would have been detected by either test.
The aim of the Familial MRI Screening Study (FaMRIsc) is to compare the efficacy of MRI screening to mammography for women with a familial risk. Furthermore, we will assess the influence of breast density.
This Dutch multicentre, randomized controlled trial, with balanced randomisation (1:1) has a parallel grouped design. Women with a cumulative lifetime risk for breast cancer due to their family history of ≥20%, aged 30–55 years are eligible. Identified BRCA1/2 mutation carriers or women with 50% risk of carrying a mutation are excluded. Group 1 receives yearly mammography and clinical breast examination (n = 1000), and group 2 yearly MRI and clinical breast examination, and mammography biennially (n = 1000).
Primary endpoints are the number and stage of the detected breast cancers in each arm. Secondary endpoints are the number of false-positive results in both screening arms. Furthermore, sensitivity and positive predictive value of both screening strategies will be assessed. Cost-effectiveness of both strategies will be assessed. Analyses will also be performed with mammographic density as stratification factor.
Personalized breast cancer screening might optimize mortality reduction with less over diagnosis. Breast density may be a key discriminator for selecting the optimal screening strategy for women < 55 years with familial breast cancer risk; mammography or MRI. These issues are addressed in the FaMRIsc study including high risk women due to a familial predisposition.
Trial registration
Netherland Trial Register NTR2789
PMCID: PMC3488502  PMID: 23031619
Breast cancer; Familial risk; Screening; MRI; Breast density; Cost-effectiveness
3.  What if I don’t treat my PSA-detected prostate cancer? Answers from three natural history models 
Making an informed decision about treating a prostate cancer detected following a routine prostate-specific antigen (PSA) test requires knowledge about disease natural history, such as the chances that it would have been clinically diagnosed in the absence of screening and that it would metastasize or lead to death in the absence of treatment.
We use three independently developed models of prostate cancer natural history to project risks of clinical progression events and disease-specific deaths for PSA-detected cases assuming they receive no primary treatment.
The three models project that 20–33% of men have preclinical onset; of these 38–50% would be clinically diagnosed and 12–25% would die of the disease in the absence of screening and primary treatment. The risk that men under age 60 at PSA detection with Gleason score 2–7 would have been clinically diagnosed in the absence of screening is 67–93% and would die of the disease in the absence of primary treatment is 23–34%. For Gleason score 8–10 these risks are 90–96% and 63–83%.
Risks of disease progression among untreated PSA-detected cases can be nontrivial, particularly for younger men and men with high Gleason scores. Model projections can be useful for informing decisions about treatment.
This is the first study to project population-based natural history summaries in the absence of screening or primary treatment and risks of clinical progression events following PSA detection in the absence of primary treatment.
PMCID: PMC3091266  PMID: 21546365
Comparative modeling; natural history; prostatic neoplasm; PSA screening
4.  Interpreting Overdiagnosis Estimates in Population-based Mammography Screening 
Epidemiologic Reviews  2011;33(1):111-121.
Estimates of overdiagnosis in mammography screening range from 1% to 54%. This review explains such variations using gradual implementation of mammography screening in the Netherlands as an example. Breast cancer incidence without screening was predicted with a micro-simulation model. Observed breast cancer incidence (including ductal carcinoma in situ and invasive breast cancer) was modeled and compared with predicted incidence without screening during various phases of screening program implementation. Overdiagnosis was calculated as the difference between the modeled number of breast cancers with and the predicted number of breast cancers without screening. Estimating overdiagnosis annually between 1990 and 2006 illustrated the importance of the time at which overdiagnosis is measured. Overdiagnosis was also calculated using several estimators identified from the literature. The estimated overdiagnosis rate peaked during the implementation phase of screening, at 11.4% of all predicted cancers in women aged 0–100 years in the absence of screening. At steady-state screening, in 2006, this estimate had decreased to 2.8%. When different estimators were used, the overdiagnosis rate in 2006 ranged from 3.6% (screening age or older) to 9.7% (screening age only). The authors concluded that the estimated overdiagnosis rate in 2006 could vary by a factor of 3.5 when different denominators were used. Calculations based on earlier screening program phases may overestimate overdiagnosis by a factor 4. Sufficient follow-up and agreement regarding the chosen estimator are needed to obtain reliable estimates.
PMCID: PMC3132806  PMID: 21709144
breast neoplasms; early detection of cancer; incidence; mammography; mass screening; overdiagnosis; risk
5.  Prostate-Specific Antigen Screening in the United States vs in the European Randomized Study of Screening for Prostate Cancer–Rotterdam 
Dissemination of prostate-specific antigen (PSA) testing in the United States coincided with an increasing incidence of prostate cancer, a shift to earlier stage disease at diagnosis, and decreasing prostate cancer mortality. We compared PSA screening performance with respect to prostate cancer detection in the US population vs in the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC–Rotterdam). We developed a simulation model for prostate cancer and PSA screening for ERSPC–Rotterdam. This model was then adapted to the US population by replacing demography parameters with US-specific ones and the screening protocol with the frequency of PSA tests in the US population. We assumed that the natural progression of prostate cancer and the sensitivity of a PSA test followed by a biopsy were the same in the United States as in ERSPC–Rotterdam. The predicted prostate cancer incidence peak in the United States was then substantially higher than the observed prostate cancer incidence peak (13.3 vs 8.1 cases per 1000 man-years). However, the actual observed incidence was reproduced by assuming a substantially lower PSA test sensitivity in the United States than in ERSPC–Rotterdam. For example, for nonpalpable local- or regional-stage cancers (ie, stage T1M0), the estimates of PSA test sensitivity were 0.26 in the United States vs 0.94 in ERSPC–Rotterdam. We conclude that the efficacy of PSA screening in detecting prostate cancer was lower in the United States than in ERSPC–Rotterdam.
PMCID: PMC2831048  PMID: 20142584
6.  What level of risk tips the balance of benefits and harms to favor screening mammography starting at age 40? 
Annals of internal medicine  2012;156(9):609-617.
In the US biennial screening mammography is recommended for average-risk women aged 50–74 because the benefits outweigh the harms. For women with increased risk starting screening at age 40 may have a similar harm-benefit ratio.
Determine the threshold relative risk (RR) at which the harm-benefit ratio of screening women aged 40–49 equals that of biennial screening for women aged 50–74.
Comparative modeling study.
Data Sources
Surveillance, Epidemiology, and End Results, Breast Cancer Surveillance Consortium, medical literature.
Target Population
A contemporary cohort of women eligible for routine screening.
Time Horizon
Mammography screening starting at age 40 vs. 50 with different screening modalities (film, digital) and screening intervals (annual, biennial).
Outcome Measures
Benefits: life-years gained, breast cancer deaths averted; harms: false-positive mammography examinations; and harm-benefit ratios: false positives/life-year gained, false positives/death averted.
Results of Base-Case Analysis
Screening average-risk women aged 50–74 biennially yields the same false positives/life-year gained as biennial screening with digital mammography starting at age 40 for women with a 2-fold increased risk above average (median threshold RR 1.9; range across models 1.5–4.4). The threshold RRs are higher for annual screening with digital mammography(median 4.3; range 3.3–10) and higher when false positives/death averted is used as outcome measure instead of false positives/life-year gained. The harm-benefit ratio for film mammography is more favorable than for digital, because film has a lower false-positive rate.
Results of Sensitivity Analysis
The threshold RRs changed slightly when a more comprehensive measure of harm was used and were relatively insensitive to lower adherence assumptions.
Risk was assumed to influence onset of disease without influencing screening performance.
Women aged 40–49 with a 2-fold increased risk have similar harm-benefit ratios for biennial screening mammography as average-risk women aged 50–74. Threshold RRs required for favorable harm-benefit ratios vary by screening modality, interval, and outcome measure.
Primary Funding Source
National Cancer Institute.
PMCID: PMC3520058  PMID: 22547470
7.  Race-Specific Impact of Natural History, Mammography Screening and Adjuvant Treatment on Breast Cancer Mortality Rates in the US 
US Black women have higher breast cancer mortality rates than White women despite lower incidence. The aim of this study is to investigate how much of the mortality disparity can be attributed to racial differences in natural history, uptake of mammography screening and use of adjuvant therapy.
Two simulation models use common national race- and age-specific data for incidence, screening and treatment dissemination, stage distributions, survival and competing mortality from 1975 to 2010. Treatment effectiveness and mammography sensitivity are assumed to be the same for both races. We sequentially substituted Black parameters into the White model to identify parameters that drive the higher mortality for Black women in the current time period.
Both models accurately reproduced observed breast cancer incidence, stage and tumor size distributions and breast cancer mortality for White women. The higher mortality for Black women could be attributed to differences in natural history parameters (26–44%), use of adjuvant therapy (11–19%) and uptake of mammography screening (7–8%), leaving 38–46% unexplained.
Black women appear to have benefited less from cancer control advances than White women, with a greater race-related gap in the use of adjuvant therapy than screening. However, a greater portion of the disparity in mortality appears to be due to differences in natural history and undetermined factors.
Breast cancer mortality may be reduced substantially by ensuring that Black women receive equal adjuvant treatment and screening as White women. More research on racial variation in breast cancer biology and treatment utilization is needed.
PMCID: PMC3075821  PMID: 21119071
breast neoplasms; mammography; adjuvant therapy; mortality; healthcare disparities; continental population groups; computer simulation

Results 1-7 (7)